I did marine biology field work almost 5 decades ago as a lowly junior lab tech. Work always has downsides, for me it was not really the Scots winter, cold feed, chapped hands, the land-rover having to reverse up steep icy roads to get back from the harbourside: it was washing the glassware and dealing with sodium hydroxide weighing (it absorbs moisture from the air so its a fools game). But, field work also brought amazing experiences, I visited the seaside 70+ times over a year, and got an insight into what a time series really means when you cover the tidal and weather and seasonal cycles.
It's also always error-prone. Nothing in the field is perfect. Reality is a bad approximation for your model at times, if you take a model centric view.
I would be immensely skeptical that field work is ever going away. There may be aspects of truth in this around cost of travel, risk, seniority.
I've always enjoyed field work, much of the code I've written has been well outside of any office.
Exploration geophysics paid for me to travel to and across more than half he countries on the planet, calibrating old maps, datums, projections against the 'new' WGS84, scaling peaks to stage base stations, getting familiar with the ins and outs of tides, magnetic fields, gravity, radiometric backgrounds, finding a good band in Mali ...
Douglas Mawson ("home of the blizzard") had a rich life after Antartica as a field geologist, exploring the flinders ranges. He found a radium mine and was shipping ore to Europe for a while. He led students on field trips, one of whom, Reg Sprigg caught the bug, explored as much as he could, persuaded the Australian petro and uranium sector to fund pushing tracks into his favourite spots, and then converted the landscape into the Arkaroola Wilderness Sanctuary. I got to spend a night there last year on a flight safari to Lake Eyre, it's an amazing place, dark sky with a big telescope, wildlife, well worth a visit.
Mawson had the field trip of a lifetime (for his two mates, it was the end of their lifetime!) and it didn't end his bug for the outside. I don't think he was made to sit in a lab.
I'd say your Mali trip was the same: it hasn't made you want to stop being outside from the sound of it.
I've "retired" to argriculture tech and labour support for W.Australian family grain production. We've almost finished harvest and I've been doing a lot of scrolling and posting here while hanging about near idle "on call" fire tenders (we had a hundred fires, mostly from lightening strikes, in a single week just recently)
The Mali trip was notable for random types firing weapons at our aircraft while we were running lines with 80m ground clearance - we had to armour the cockpit bellies and stuff the fuel tanks with mesh.
I also have spent quite awhile as an exploration geophysicist. I miss it! I work purely with satellite data now, which is decidedly less tangible.
I've done a fair bit in the field, but a huge part of my career has been mining old datasets and reinterpreting things in light of new data/etc.
What the article is describing isn't new in any way. But it also doesn't remove the need for fieldwork or the need for the experience of having done fieldwork to use existing datasets. Observational sciences (e.g. geology, biology, etc) where you can't easily replicate the environment you are studying in the lab are always going to hinge on some sort of fieldwork.
Finding creative ways to use existing data doesn't change that.
I worked in a research lab like 30 years ago and it was all on computers. We had loads of generic data collected by someone somewhere and we just looked for patterns to infer sequences. I wrote Java and C++ and got my name on a paper. There were maybe a dozen scientists in the lab and they were all just coders with expertise in one or another field of biology. It was called a "dry lab".
I don't do research that requires fieldwork, but even in office and industrial settings, I notice that there's less need and interest in visits.
Of course, in-person exchanges still happen, but there's something of a default to do most things remotely because it's more efficient (and honestly, easier for all parties involved). The result is that you don't get to see cool or unusual machines/setups that often, and some flair of doing research is lost.
I can imagine that that's especially painful for new ecologists, because fieldwork is also a way to experience things that you otherwise wouldn't. Hopefully, we can bring some of it back with edge devices and models.
It’s been sad seeing journalism in the online era, where so much (not all!) content is produced without really visiting or researching things. Often it’s based only on statements / tweets, sometimes more seeping based on phone calls, sometimes reading a book on the topic, but rarely do journalists seem to show up anywhere.
When reading something like Didion’s piece on the LA highway central command, it shows how irreplaceable lived experience is.
> But, field work also brought amazing experiences, I visited the seaside 70+ times over a year, and got an insight into what a time series really means when you cover the tidal and weather and seasonal cycles.
I’m not exactly sure if we share a similar experience, but living on a trail in the Santa Cruz mountains affords me the opportunity to hike the same trails every weekend, year round (or even daily).
I’m not taking measurements, but it’s incredible to witness the effect of seasons on familiar territory just a few miles outside town. The weather changes, the wildlife changes and the air changes (moist to dry and back).
It’s an incredibly special experience to revisit the same place time and time again and witness the impact of … time. I hope you found something else to replace your familiar seaside.
I think this is a classic old-vs-new tale. I started my PhD in biochemical research where analyzing data by hand was definitely a "craft" in some aspects. Later I forewent going to the lab entirely and instead spent all my time on developing machine learning for automated data analysis. But just like field work, you still need people in labs who can continue the craft.
The article should perhaps introspect a bit more instead of setting up a false dichotomy between "rainforest field work or computers".
Field work is likely to become less common, because ‘ground truth’ (as error prone as it may be), is an existential threat to a lot of people’s comforts and state of mind right now.
After all, if you want a different answer, you can easily tweak a model.
Much harder to force a bunch of people in the field to not see what they see. Certainly not impossible, however!
Working in geology, I find the opposite problem. Field work is so highly valued that we're at a place where we have so much data and not enough people really working and analyzing it. My general impression is that in some subfields work that's done exclusively using preexisting data is kind of looked down on. In my opinion tons and tons of money is essentially wasted collecting new data - and then it's poorly catalogued and hard to access. You typically have to email some author and hope they send you the data. People are fiercely protective of their data b/c it took a lot of effort to collect and they want credit and to be in on any derivative work (and not just a reference at the bottom of a paper)
I would say the main workflow is collect some new data nobody has collect before, look at it and see if it shows anything interesting, make up some interesting publishable interpretation.
It feels like it'd be smarter to start with working with existing data and publish that way. If you hit on some specific missing piece, go collect that data, and work from there. But the incentive structures aren't aligned with this
The AI angle is really shoehorned in, but irrelevant to the larger problem. Sure, it allows you to annotate more data. Obviously it's more fun to go do field work than count pollen grains under a microscope. If anything AI make it easier to do more fieldwork and collect even more data b/c now you can in-theory crunch it faster
The current situation with the way big tech plays fast and loose with other people's data, I don't suppose the siloed nature of geological data is going to get better any time soon.
Perhaps creating secure private clouds for scientists, away from AI scrapers etc that scientists can access, with associated counter-surveillance, is the way forward.
I'm a GIS guy working on cloud native tech, but with a focus on privacy. I have a local-first Mac native product nearing beta. I'm thinking a lot about what data sharing options can be at the moment.
i dont see what the problem is. AI is mostly irrelevant. okay they scrape your data.. but then what? If the data isn't offically published and doesnt have a DOI, anything built on that wont be accepted
Some people scrape charts in publications to extract data. This has been done for a while. Maybe AI could automate this step. Thatd be useful
I understand that publications are the currency of academics but they're largely irrelevant in business. Geological data are valuable and if an oil exploration company finds a nice dataset they can scrape, they're not going to publish it.
From a pure business perspective, AI is largely about copyright circumvention. The laws are lagging and people are making serious money from data theft.
This is largely solved in biomedicine by funders (not journals) and regulatory bodies requiring that human subjects research data be stored with NIH.
I guess there may be a broader and less public-oriented set of funders in geology- and maybe there aren’t as many standardized data types as there are in the world of biology.
Sounds like a business opportunity for someone to create a web portal for making available such data with licensing terms, indexing and cataloging it with a nice search engine, etc.
what you need is people uploading data in consistent well documented formats. There are all sorts or projects that do this, but there is a strong incentive to not upload things, or sort of half upload it.. but in a way where anyone using it is going to have to reach out to you. Not suggesting bad intentions, Maybe youre still working with the data and expect to publish more and dont want someone swooping in and beating you to the punch. Typically journals require data availability, but its kind of informal and adhoc
I am involved in both botanical field work and ML, unfortunately most of the data that I have gathered and analyzed in the last 12 years indicates how quickly many ecosystems are degrading. Often I wonder why I do the analysis, simply taking a photograph in the same spot ~7 years apart allows any average person to see that things are not on a positive trajectory.
Attempting to convince people to change course and focus on restoration has mostly been a losing battle, with much larger forces behind the main detriments that make local changes feel inadequate.
I feel the same with experimental biology with animals. Nowadays, you need fancy computational models with loads of data to publish in good journals. The reason: because everyone else is doing it. The days of elegant, simple experimental design are gone for the better or the worse.
As a kid I had problems with Foundation (Asimov) premise that loss of scientific knowledge can be the trigger not just the result of civilizational collapse - not anymore.
More pernicious than loss of knowledge imo is loss of trust into the scientific process. „Research“ on social media is seen as superior to expert consensus by a frightening number of people.
People that trust something without understanding at least the principles can cause quite some issues.
One of the issues that I think affects these people is that the scientific process can not guarantee that something is "correct" or "incorrect". Something that at a point was known to be correct can be later disproved by a later experiment or more specific conditions.
Some people want/need simple certainties, and as soon as they stumble upon something different, they will shift their trust to something "simple and clear". And they can do that again and again, as long as they don't need to accept some things are complex or unknown.
I do not know any school system (not that I am an expert or searched for it, just an impression) that emphasizes this dynamic nature of understanding, or that tries to make people accept the unknown.
Random thought: language seems to have evolved as part of social interactions to facilitate group dynamics in some way. So people will privilege linguistic 'transactions' with other people over some kind of formal development of an argument from evidence.
(Been reading some of Wojcieck Zurek's semi-pop articles on decohrerence, and he alway puts a paragraph about the evolution of language in and points out that our intuitions won't work well in the quantum domain).
You can thank journalists for that. "Expert" opinions have been overused to control public opinion during COVID times, and several of the expert opinions have turned out to be wrong. People remember that, and distrust is the logical result.
Yep -a single look at an "expert" in computing from any recent articles shows the problem first hand. There's a massive issue with a focus on credentialism first, which is leading to a distrust of experts in general.
My post above was downvoted. Thankfully some kind souls have redeemed it. I'm surprised it wasn't flagged and disappeared to Gitmo, like 90% of my completely innocuous posts are.
I'm a touch confused, the lead in has "plants shift their flowering times to cope with the heat, rather than adapting through natural selection." How is that not just part of natural selection? Plants that are unable to shifting their flowering time will die off. No?
It's not worded very well, but the distinction has to do with whether the plant has a built-in system for adjusting flowering times based on heat (all necessary code already present, no genetic mutations needed for it to adjust flower timing), or whether the shift in flowering times is directly caused by favorable genetic mutations.
It's a bit like the distinction between being able to swim around, like a fish, versus just having offspring who end up a bit closer to some goal, like a coral. Both end up in a place where they can live, but only one got there by being the lucky survivor against a background of alternatives that were born elsewhere.
It's made clear in the title of one of their papers: "Plasticity and not adaptation is the primary source of temperature-mediated variation in flowering phenology in North America" (https://pubmed.ncbi.nlm.nih.gov/38212525/)
I think that makes some sense, but I would think that is still part and parcel for natural selection? To require specific mechanisms just feels wrong.
Granted, my teaching for what drives evolution is colored by the idea of the peppered moth, which clearly had everything necessary for both colors. The population change on dominant color, though, was driven by environmental changes.
Even that title you give seems to preclude that increased plasticity can itself be an adaptation? Wouldn't we need to show that there was no change in plasticity during this time in order to show otherwise?
It's a meaningful distinction because adaptation has the potential to introduce novel mechanisms while plasticity doesn't. So if you mistook one for the other, you might assume that a given crop will continue to adapt to a warming climate when what's actually happening is that it's approaching the bounds of its plasticity and yet another degree of warming will cause it to fail.
You could also make bad assumptions related to rates. One might assume that adaptation will occur within some time window when actually they're extrapolating based on plasticity.
Sure, plasticity and its limits are themselves the result of natural selection, but there are still experiments you can do to distinguish the two (e.g. does the change show up in the genome versus the transcriptome or the proteome?). I guess I'm just uncertain about how useful it is to split hairs beyond a certain point. Like, you could say that everything is caused by the big bang, case closed, but that's not going to affect any subsequent decisions that people might make.
Machine learning and data science are not new things in science. It's great that we have the ability to share and work with existing data sets, collect data remotely with sensors, and build software to create models, but we'll always need people to go out and collect updated data, place censors and verify that what models predict is actually happening.
> Scientists who run long-term ecological studies, in particular, report that they struggle to find funding.
It's cheaper and easier to do stuff sitting at a desk. In theory that's a good thing if it means more work gets done, but field work has to happen too. For many people it's the best part of the job, for others it's a pain that has to be suffered through to get the data they need. Hopefully there's room (and funding) for both kinds of people to do the work they want.
I'm a scientist in industry. It's remarkable how many smart people think that science can be done without data. I've heard managers ask: "Why do we need to gather data? Can't we just model it? The customer doesn't want to see data. They just want an answer."
There's also a strong belief in "statistical magic." Faced with a bad or insufficient data set, someone will say: "Let's give the data to <statistician> and have them work their magic on it."
That the results actually have to be influenced by the data in some way is something that has to be explained to people. In all of my years as a scientist, I've learned that there's still no substitute for good measurements. Good data can be cheaper than analysis of bad data.
Data science is not fundamentally about data or science. It's about either justifying decisions that have already been made or delegating decisions to an unbiased casting of bones to let the gods decide.
The specific problem he was facing was looking a DNA polymerases - and the problem with enzymes is they are catalytic - so a 1% impurity at the protein level might account for 100% of the activity you are measuring and wrongly ascribing to the 99% of purified protein.
So much of science is trying not to fool yourself.
> It's remarkable how many smart people think that science can be done without data.
It’s so important that we write these down, so when these people have forgotten why they’re not making any progress and they’re searching for answers, they’ll find what we wrote down and say “ohhh, we had too much hubris thought we were smarter than everyone else and didn’t listen to how important actually going outside is.”
People that I'm currently working with are using AI to try to extract data from the text of published papers, getting access to raw data sets doesn't seem to be a priority.
It should be easily accessed without even having to ask (links should be automatically provided and maintained by whatever entity published the paper).
I have a PhD in Ecology and a BS in CS. I find the bifurcation portrayed here exaggerated. The best modern ecologists merge rigorous fieldwork with advanced modeling; we need to harness vast, underutilized datasets, not just generate new ones.
The 'computer scientist' quote illustrates a frustrating trend: tech-centric 'drive-bys' that lack the ecological context required for good science. On the flip side, the 'old guard' who ignore modern data assimilation are leaving massive potential on the table. The field is rightfully shifting from site-specific anecdotes to foundational, broad-scale work, but we need both skillsets to do it justice.
Ecology PhD turned data scientist, I was looking to respond and you summed up my thoughts really well!
I will add that funding can complicate things a bit, funding sources often get wowed by more "advanced" methods, while the underlying science might be less than stellar. There are important questions that can be answered by small, elegant field studies, and there are questions that require larger datasets and more computation. When we start putting the methodological cart before the scientific horse, that's where we run into problems.
I'd also add that the best scientists I know have, for the duration of their careers, put the question first and pursued methods to fit. I know folks who have the wildest set of skills, from next-gen sequencing to fish tattooing and all sorts of random engineering skills. Willingness to learn new skills in the pursuit of worthwhile questions is one of the hallmarks of a good scientist, in my experience.
100 percent. I'm guilty of doing this wrong in the beginning of my PhD, and it was the biggest hurdle I needed to learn how to overcome. It's easy to try to force problems onto methodologies; it's much harder (and more interesting!) to try to solve real problems with the best available tool for the job.
Seems to me there are potentially opportunities for greater returns to data gathering work as quality data can inform many more papers in the future. How that will work still needs to be brokered…
Absolutely. There are a few excellent projects in this vein, as well - where deeper investment in data gathering, done in ways to optimize its broad use in research, is occurring.
An example is the National Science Foundation NEON project, which is a long-term ecological monitoring initiative with common field methodologies across 81 North American sites. https://www.neonscience.org/
Huh. I weirdly enough have worked with a lot of those sites from the remote sensing side, but never really know what the overall project was. Just "use the NEON sites for examples". I should have looked it up more at the time. Thanks for sharing!
There is still field work that happens. AI can not replace that. You'd need to literally simulate the whole world before AI can even get close to gather all data obtainable here. For instance, on a given ant hill: which plant species will be more prevalent there? (For those not knowing a lot about ants: some ant species carry specific plants or defend plants against invaders. The most usual example is for leaf-cutting ants, but there are many additional examples, and for various reasons you will also find different plant species to be more prevalent close to an ant hill in a forest area, than other plant species.) AI can steal existing data, but there is no way it can gather real data UNLESS you are able to monitor this. This is possible via machines, e. g. drones, but AI does not understand what it is doing and even with instructions you still may be able to just hallucinate data. So perhaps one day this may all be fully automated (sensor systems can do all humans can do too, of course), but right now this is simply not the case. And this is just one example for many more.
For example often if you have a blackfly infestation, the first sign is often a steady stream of ants - they are feeding of the honeydew created by the blackfly, they protect the blackfly from predators like ladybird larvae, and they will even transfer the blackfly to new plants.
Once I knew this I found the best way to tackle blackfly was not to go after the blackfly, but distract the ants - a bit of jam works a treat.
Though you could then argue the ant's have then moved on, from farming, to a protection racket.
"Others have expressed concern about ‘AI colonialism’, a practice in which data, collected remotely in poorer countries, are siphoned off for analysis in well-equipped labs elsewhere."
That’s not what the article says at all, but go on with your doomer takes.
Instead of counting bears in the forest by hand, you set up a hundred trail cameras and then use computers to count bears 24/7 across an entire area. This is field research, on a scale that was previously impossible.
All of your peaceful science drones (just the same as attack drones) will have to be equipped with countermeasures such as flares and chaff, to defend against the primitive jury rigged SAM systems and anti-aircraft batteries the Forest People of 2056 will use to shoot down anything that intrudes upon their domain, years after the Great War (No, For Real This Time ZOMG) destroys half the planet and now it's the Hunger Games future they have planned for us.
I did marine biology field work almost 5 decades ago as a lowly junior lab tech. Work always has downsides, for me it was not really the Scots winter, cold feed, chapped hands, the land-rover having to reverse up steep icy roads to get back from the harbourside: it was washing the glassware and dealing with sodium hydroxide weighing (it absorbs moisture from the air so its a fools game). But, field work also brought amazing experiences, I visited the seaside 70+ times over a year, and got an insight into what a time series really means when you cover the tidal and weather and seasonal cycles.
It's also always error-prone. Nothing in the field is perfect. Reality is a bad approximation for your model at times, if you take a model centric view.
I would be immensely skeptical that field work is ever going away. There may be aspects of truth in this around cost of travel, risk, seniority.
I've always enjoyed field work, much of the code I've written has been well outside of any office.
Exploration geophysics paid for me to travel to and across more than half he countries on the planet, calibrating old maps, datums, projections against the 'new' WGS84, scaling peaks to stage base stations, getting familiar with the ins and outs of tides, magnetic fields, gravity, radiometric backgrounds, finding a good band in Mali ...
Loved it.
Douglas Mawson ("home of the blizzard") had a rich life after Antartica as a field geologist, exploring the flinders ranges. He found a radium mine and was shipping ore to Europe for a while. He led students on field trips, one of whom, Reg Sprigg caught the bug, explored as much as he could, persuaded the Australian petro and uranium sector to fund pushing tracks into his favourite spots, and then converted the landscape into the Arkaroola Wilderness Sanctuary. I got to spend a night there last year on a flight safari to Lake Eyre, it's an amazing place, dark sky with a big telescope, wildlife, well worth a visit.
Mawson had the field trip of a lifetime (for his two mates, it was the end of their lifetime!) and it didn't end his bug for the outside. I don't think he was made to sit in a lab.
I'd say your Mali trip was the same: it hasn't made you want to stop being outside from the sound of it.
Not in the least, I still love the outdoors.
I've "retired" to argriculture tech and labour support for W.Australian family grain production. We've almost finished harvest and I've been doing a lot of scrolling and posting here while hanging about near idle "on call" fire tenders (we had a hundred fires, mostly from lightening strikes, in a single week just recently)
* https://www.watoday.com.au/national/western-australia/wa-bus...
* https://www.youtube.com/watch?v=yulvSvtFVqc
^ Further south than I'm based, and a header fire, not a strike. Okay when caught early - life and town threatening if not.
Oh, yeah: Songhoy Blues: https://www.youtube.com/watch?v=BOValSt7YOY
The Mali trip was notable for random types firing weapons at our aircraft while we were running lines with 80m ground clearance - we had to armour the cockpit bellies and stuff the fuel tanks with mesh.
I also have spent quite awhile as an exploration geophysicist. I miss it! I work purely with satellite data now, which is decidedly less tangible.
I've done a fair bit in the field, but a huge part of my career has been mining old datasets and reinterpreting things in light of new data/etc.
What the article is describing isn't new in any way. But it also doesn't remove the need for fieldwork or the need for the experience of having done fieldwork to use existing datasets. Observational sciences (e.g. geology, biology, etc) where you can't easily replicate the environment you are studying in the lab are always going to hinge on some sort of fieldwork.
Finding creative ways to use existing data doesn't change that.
I worked in a research lab like 30 years ago and it was all on computers. We had loads of generic data collected by someone somewhere and we just looked for patterns to infer sequences. I wrote Java and C++ and got my name on a paper. There were maybe a dozen scientists in the lab and they were all just coders with expertise in one or another field of biology. It was called a "dry lab".
I don't do research that requires fieldwork, but even in office and industrial settings, I notice that there's less need and interest in visits.
Of course, in-person exchanges still happen, but there's something of a default to do most things remotely because it's more efficient (and honestly, easier for all parties involved). The result is that you don't get to see cool or unusual machines/setups that often, and some flair of doing research is lost.
I can imagine that that's especially painful for new ecologists, because fieldwork is also a way to experience things that you otherwise wouldn't. Hopefully, we can bring some of it back with edge devices and models.
I hope it doesn’t go away, too.
It’s been sad seeing journalism in the online era, where so much (not all!) content is produced without really visiting or researching things. Often it’s based only on statements / tweets, sometimes more seeping based on phone calls, sometimes reading a book on the topic, but rarely do journalists seem to show up anywhere.
When reading something like Didion’s piece on the LA highway central command, it shows how irreplaceable lived experience is.
> But, field work also brought amazing experiences, I visited the seaside 70+ times over a year, and got an insight into what a time series really means when you cover the tidal and weather and seasonal cycles.
I’m not exactly sure if we share a similar experience, but living on a trail in the Santa Cruz mountains affords me the opportunity to hike the same trails every weekend, year round (or even daily).
I’m not taking measurements, but it’s incredible to witness the effect of seasons on familiar territory just a few miles outside town. The weather changes, the wildlife changes and the air changes (moist to dry and back).
It’s an incredibly special experience to revisit the same place time and time again and witness the impact of … time. I hope you found something else to replace your familiar seaside.
I think this is a classic old-vs-new tale. I started my PhD in biochemical research where analyzing data by hand was definitely a "craft" in some aspects. Later I forewent going to the lab entirely and instead spent all my time on developing machine learning for automated data analysis. But just like field work, you still need people in labs who can continue the craft.
The article should perhaps introspect a bit more instead of setting up a false dichotomy between "rainforest field work or computers".
Field work is likely to become less common, because ‘ground truth’ (as error prone as it may be), is an existential threat to a lot of people’s comforts and state of mind right now.
After all, if you want a different answer, you can easily tweak a model.
Much harder to force a bunch of people in the field to not see what they see. Certainly not impossible, however!
> I would be immensely skeptical that field work is ever going away. There may be aspects of truth in this around cost of travel, risk, seniority.
Why be sceptical? The model will answer consistently, inconvenient truths won't get in the way.
If it disagrees with experiment, it's wrong.
https://youtu.be/EYPapE-3FRw?si=LjSNyltYaVUY5bzX
Working in geology, I find the opposite problem. Field work is so highly valued that we're at a place where we have so much data and not enough people really working and analyzing it. My general impression is that in some subfields work that's done exclusively using preexisting data is kind of looked down on. In my opinion tons and tons of money is essentially wasted collecting new data - and then it's poorly catalogued and hard to access. You typically have to email some author and hope they send you the data. People are fiercely protective of their data b/c it took a lot of effort to collect and they want credit and to be in on any derivative work (and not just a reference at the bottom of a paper)
I would say the main workflow is collect some new data nobody has collect before, look at it and see if it shows anything interesting, make up some interesting publishable interpretation.
It feels like it'd be smarter to start with working with existing data and publish that way. If you hit on some specific missing piece, go collect that data, and work from there. But the incentive structures aren't aligned with this
The AI angle is really shoehorned in, but irrelevant to the larger problem. Sure, it allows you to annotate more data. Obviously it's more fun to go do field work than count pollen grains under a microscope. If anything AI make it easier to do more fieldwork and collect even more data b/c now you can in-theory crunch it faster
The current situation with the way big tech plays fast and loose with other people's data, I don't suppose the siloed nature of geological data is going to get better any time soon.
Perhaps creating secure private clouds for scientists, away from AI scrapers etc that scientists can access, with associated counter-surveillance, is the way forward.
I'm a GIS guy working on cloud native tech, but with a focus on privacy. I have a local-first Mac native product nearing beta. I'm thinking a lot about what data sharing options can be at the moment.
i dont see what the problem is. AI is mostly irrelevant. okay they scrape your data.. but then what? If the data isn't offically published and doesnt have a DOI, anything built on that wont be accepted
Some people scrape charts in publications to extract data. This has been done for a while. Maybe AI could automate this step. Thatd be useful
I understand that publications are the currency of academics but they're largely irrelevant in business. Geological data are valuable and if an oil exploration company finds a nice dataset they can scrape, they're not going to publish it.
From a pure business perspective, AI is largely about copyright circumvention. The laws are lagging and people are making serious money from data theft.
This is largely solved in biomedicine by funders (not journals) and regulatory bodies requiring that human subjects research data be stored with NIH.
I guess there may be a broader and less public-oriented set of funders in geology- and maybe there aren’t as many standardized data types as there are in the world of biology.
Sounds like a business opportunity for someone to create a web portal for making available such data with licensing terms, indexing and cataloging it with a nice search engine, etc.
the databases exist, for instance: https://www.pangaea.de/
what you need is people uploading data in consistent well documented formats. There are all sorts or projects that do this, but there is a strong incentive to not upload things, or sort of half upload it.. but in a way where anyone using it is going to have to reach out to you. Not suggesting bad intentions, Maybe youre still working with the data and expect to publish more and dont want someone swooping in and beating you to the punch. Typically journals require data availability, but its kind of informal and adhoc
I am involved in both botanical field work and ML, unfortunately most of the data that I have gathered and analyzed in the last 12 years indicates how quickly many ecosystems are degrading. Often I wonder why I do the analysis, simply taking a photograph in the same spot ~7 years apart allows any average person to see that things are not on a positive trajectory.
Attempting to convince people to change course and focus on restoration has mostly been a losing battle, with much larger forces behind the main detriments that make local changes feel inadequate.
I can relate to this, it is what ultimately caused me to leave the field of ecology/conservation. It all felt depressingly pointless.
Can you give more detail about the degradation you're seeing? What sort of work are you doing? Thanks. I'm interested to hear about this.
I'm guessing invasives taking over. We need to not be so prissy about biological control.
I feel the same with experimental biology with animals. Nowadays, you need fancy computational models with loads of data to publish in good journals. The reason: because everyone else is doing it. The days of elegant, simple experimental design are gone for the better or the worse.
As a kid I had problems with Foundation (Asimov) premise that loss of scientific knowledge can be the trigger not just the result of civilizational collapse - not anymore.
More pernicious than loss of knowledge imo is loss of trust into the scientific process. „Research“ on social media is seen as superior to expert consensus by a frightening number of people.
People that trust something without understanding at least the principles can cause quite some issues.
One of the issues that I think affects these people is that the scientific process can not guarantee that something is "correct" or "incorrect". Something that at a point was known to be correct can be later disproved by a later experiment or more specific conditions.
Some people want/need simple certainties, and as soon as they stumble upon something different, they will shift their trust to something "simple and clear". And they can do that again and again, as long as they don't need to accept some things are complex or unknown.
I do not know any school system (not that I am an expert or searched for it, just an impression) that emphasizes this dynamic nature of understanding, or that tries to make people accept the unknown.
Random thought: language seems to have evolved as part of social interactions to facilitate group dynamics in some way. So people will privilege linguistic 'transactions' with other people over some kind of formal development of an argument from evidence.
(Been reading some of Wojcieck Zurek's semi-pop articles on decohrerence, and he alway puts a paragraph about the evolution of language in and points out that our intuitions won't work well in the quantum domain).
You can thank journalists for that. "Expert" opinions have been overused to control public opinion during COVID times, and several of the expert opinions have turned out to be wrong. People remember that, and distrust is the logical result.
Yep -a single look at an "expert" in computing from any recent articles shows the problem first hand. There's a massive issue with a focus on credentialism first, which is leading to a distrust of experts in general.
IMO this is older than Covid and goes back at least to „nine out of ten doctors recommend camel cigarettes“
Hehe, I've also read the whole Foundation series, and also feel like the empire's collapse is unfolding right in front of our eyes.
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My post above was downvoted. Thankfully some kind souls have redeemed it. I'm surprised it wasn't flagged and disappeared to Gitmo, like 90% of my completely innocuous posts are.
I'm a touch confused, the lead in has "plants shift their flowering times to cope with the heat, rather than adapting through natural selection." How is that not just part of natural selection? Plants that are unable to shifting their flowering time will die off. No?
It's not worded very well, but the distinction has to do with whether the plant has a built-in system for adjusting flowering times based on heat (all necessary code already present, no genetic mutations needed for it to adjust flower timing), or whether the shift in flowering times is directly caused by favorable genetic mutations.
It's a bit like the distinction between being able to swim around, like a fish, versus just having offspring who end up a bit closer to some goal, like a coral. Both end up in a place where they can live, but only one got there by being the lucky survivor against a background of alternatives that were born elsewhere.
It's made clear in the title of one of their papers: "Plasticity and not adaptation is the primary source of temperature-mediated variation in flowering phenology in North America" (https://pubmed.ncbi.nlm.nih.gov/38212525/)
I think that makes some sense, but I would think that is still part and parcel for natural selection? To require specific mechanisms just feels wrong.
Granted, my teaching for what drives evolution is colored by the idea of the peppered moth, which clearly had everything necessary for both colors. The population change on dominant color, though, was driven by environmental changes.
Even that title you give seems to preclude that increased plasticity can itself be an adaptation? Wouldn't we need to show that there was no change in plasticity during this time in order to show otherwise?
It's a meaningful distinction because adaptation has the potential to introduce novel mechanisms while plasticity doesn't. So if you mistook one for the other, you might assume that a given crop will continue to adapt to a warming climate when what's actually happening is that it's approaching the bounds of its plasticity and yet another degree of warming will cause it to fail.
You could also make bad assumptions related to rates. One might assume that adaptation will occur within some time window when actually they're extrapolating based on plasticity.
Sure, plasticity and its limits are themselves the result of natural selection, but there are still experiments you can do to distinguish the two (e.g. does the change show up in the genome versus the transcriptome or the proteome?). I guess I'm just uncertain about how useful it is to split hairs beyond a certain point. Like, you could say that everything is caused by the big bang, case closed, but that's not going to affect any subsequent decisions that people might make.
Machine learning and data science are not new things in science. It's great that we have the ability to share and work with existing data sets, collect data remotely with sensors, and build software to create models, but we'll always need people to go out and collect updated data, place censors and verify that what models predict is actually happening.
> Scientists who run long-term ecological studies, in particular, report that they struggle to find funding.
It's cheaper and easier to do stuff sitting at a desk. In theory that's a good thing if it means more work gets done, but field work has to happen too. For many people it's the best part of the job, for others it's a pain that has to be suffered through to get the data they need. Hopefully there's room (and funding) for both kinds of people to do the work they want.
I'm a scientist in industry. It's remarkable how many smart people think that science can be done without data. I've heard managers ask: "Why do we need to gather data? Can't we just model it? The customer doesn't want to see data. They just want an answer."
There's also a strong belief in "statistical magic." Faced with a bad or insufficient data set, someone will say: "Let's give the data to <statistician> and have them work their magic on it."
That the results actually have to be influenced by the data in some way is something that has to be explained to people. In all of my years as a scientist, I've learned that there's still no substitute for good measurements. Good data can be cheaper than analysis of bad data.
Data science is not fundamentally about data or science. It's about either justifying decisions that have already been made or delegating decisions to an unbiased casting of bones to let the gods decide.
I always liked the Arthur Kornberg quote,
'don't waste clean thought on a dirty enzyme'
The specific problem he was facing was looking a DNA polymerases - and the problem with enzymes is they are catalytic - so a 1% impurity at the protein level might account for 100% of the activity you are measuring and wrongly ascribing to the 99% of purified protein.
So much of science is trying not to fool yourself.
> It's remarkable how many smart people think that science can be done without data.
It’s so important that we write these down, so when these people have forgotten why they’re not making any progress and they’re searching for answers, they’ll find what we wrote down and say “ohhh, we had too much hubris thought we were smarter than everyone else and didn’t listen to how important actually going outside is.”
People that I'm currently working with are using AI to try to extract data from the text of published papers, getting access to raw data sets doesn't seem to be a priority.
The data is supposed to be available from the authors in almost all cases, but in many (most?) cases the authors won't provide it.
It should be easily accessed without even having to ask (links should be automatically provided and maintained by whatever entity published the paper).
I have a PhD in Ecology and a BS in CS. I find the bifurcation portrayed here exaggerated. The best modern ecologists merge rigorous fieldwork with advanced modeling; we need to harness vast, underutilized datasets, not just generate new ones.
The 'computer scientist' quote illustrates a frustrating trend: tech-centric 'drive-bys' that lack the ecological context required for good science. On the flip side, the 'old guard' who ignore modern data assimilation are leaving massive potential on the table. The field is rightfully shifting from site-specific anecdotes to foundational, broad-scale work, but we need both skillsets to do it justice.
Ecology PhD turned data scientist, I was looking to respond and you summed up my thoughts really well!
I will add that funding can complicate things a bit, funding sources often get wowed by more "advanced" methods, while the underlying science might be less than stellar. There are important questions that can be answered by small, elegant field studies, and there are questions that require larger datasets and more computation. When we start putting the methodological cart before the scientific horse, that's where we run into problems.
I'd also add that the best scientists I know have, for the duration of their careers, put the question first and pursued methods to fit. I know folks who have the wildest set of skills, from next-gen sequencing to fish tattooing and all sorts of random engineering skills. Willingness to learn new skills in the pursuit of worthwhile questions is one of the hallmarks of a good scientist, in my experience.
100 percent. I'm guilty of doing this wrong in the beginning of my PhD, and it was the biggest hurdle I needed to learn how to overcome. It's easy to try to force problems onto methodologies; it's much harder (and more interesting!) to try to solve real problems with the best available tool for the job.
Seems to me there are potentially opportunities for greater returns to data gathering work as quality data can inform many more papers in the future. How that will work still needs to be brokered…
Absolutely. There are a few excellent projects in this vein, as well - where deeper investment in data gathering, done in ways to optimize its broad use in research, is occurring.
An example is the National Science Foundation NEON project, which is a long-term ecological monitoring initiative with common field methodologies across 81 North American sites. https://www.neonscience.org/
Huh. I weirdly enough have worked with a lot of those sites from the remote sensing side, but never really know what the overall project was. Just "use the NEON sites for examples". I should have looked it up more at the time. Thanks for sharing!
It leads to hoarding data (or artifacts) within labs for exclusive analysis.
This. It's this broken publishing model we have at the moment that is the real problem.
You should be able to publish data as a paper and get academic credit for doing that. Then others can publish analyses of that data, crediting you.
The article is strange. And, also not convincing.
There is still field work that happens. AI can not replace that. You'd need to literally simulate the whole world before AI can even get close to gather all data obtainable here. For instance, on a given ant hill: which plant species will be more prevalent there? (For those not knowing a lot about ants: some ant species carry specific plants or defend plants against invaders. The most usual example is for leaf-cutting ants, but there are many additional examples, and for various reasons you will also find different plant species to be more prevalent close to an ant hill in a forest area, than other plant species.) AI can steal existing data, but there is no way it can gather real data UNLESS you are able to monitor this. This is possible via machines, e. g. drones, but AI does not understand what it is doing and even with instructions you still may be able to just hallucinate data. So perhaps one day this may all be fully automated (sensor systems can do all humans can do too, of course), but right now this is simply not the case. And this is just one example for many more.
Ants also farm other insects.
For example often if you have a blackfly infestation, the first sign is often a steady stream of ants - they are feeding of the honeydew created by the blackfly, they protect the blackfly from predators like ladybird larvae, and they will even transfer the blackfly to new plants.
Once I knew this I found the best way to tackle blackfly was not to go after the blackfly, but distract the ants - a bit of jam works a treat.
Though you could then argue the ant's have then moved on, from farming, to a protection racket.
Why study the territory, when you have a map that's been conveniently generated to obfuscate any pesky discovery-indicating outliers?
I am overall pro-AI, but using it to forego uncharted territory is an incredible waste. We always need new and better data.
for a while now the work in phd/academia rarely involved 'field work'.
90% of the time it is spend analyzing data or writing up proposals/grants/papers. i don't think AI was the turning point.
"Others have expressed concern about ‘AI colonialism’, a practice in which data, collected remotely in poorer countries, are siphoned off for analysis in well-equipped labs elsewhere."
Someone is jumping the shark.
> ‘I rarely get outside’: scientists ditch fieldwork in the age of AI
that's the original title before editorializarion
Big Mother?
All right... science for hikikomoris...
I always felt like one of the primary motivations to pursue science was being able to bail out of the office for the entire summer for "field work"...
So all of our research is basically going to be theoretical and restricted to the field research done so far. Wonderful.
That’s not what the article says at all, but go on with your doomer takes.
Instead of counting bears in the forest by hand, you set up a hundred trail cameras and then use computers to count bears 24/7 across an entire area. This is field research, on a scale that was previously impossible.
We can always deploy fleets of drones to gather new data for us, right?
In the future...
All of your peaceful science drones (just the same as attack drones) will have to be equipped with countermeasures such as flares and chaff, to defend against the primitive jury rigged SAM systems and anti-aircraft batteries the Forest People of 2056 will use to shoot down anything that intrudes upon their domain, years after the Great War (No, For Real This Time ZOMG) destroys half the planet and now it's the Hunger Games future they have planned for us.